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Spatial process modelling for univariate and multivariate dynamic spatial data

Publication ,  Journal Article
Gelfand, AE; Banerjee, S; Gamerman, D
Published in: Environmetrics
August 1, 2005

There is a considerable literature in spatiotemporal modelling. The approach adopted here applies to the setting where space is viewed as continuous but time is taken to be discrete. We view the data as a time series of spatial processes and work in the setting of dynamic models, achieving a class of dynamic models for such data. We seek rich, flexible, easy-to-specify, easy-to-interpret, computationally tractable specifications which allow very general mean structures and also non-stationary association structures. Our modelling contributions are as follows. In the case where univariate data are collected at the spatial locations, we propose the use of a spatiotemporally varying coefficient form. In the case where multivariate data are collected at the locations, we need to capture associations among measurements at a given location and time as well as dependence across space and time. We propose the use of suitable multivariate spatial process models developed through coregionalization. We adopt a Bayesian inference framework. The resulting posterior and predictive inference enables summaries in the form of tables and maps, which help to reveal the nature of the spatiotemporal behaviour as well as the associated uncertainty. We illuminate various computational issues and then apply our models to the analysis of climate data obtained from the National Center for Atmospheric Research to analyze precipitation and temperature measurements obtained in Colorado in 1997. Copyright © 2005 John Wiley & Sons, Ltd.

Duke Scholars

Published In

Environmetrics

DOI

ISSN

1180-4009

Publication Date

August 1, 2005

Volume

16

Issue

5

Start / End Page

465 / 479

Related Subject Headings

  • Statistics & Probability
  • 49 Mathematical sciences
  • 41 Environmental sciences
  • 05 Environmental Sciences
  • 01 Mathematical Sciences
 

Citation

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Gelfand, A. E., Banerjee, S., & Gamerman, D. (2005). Spatial process modelling for univariate and multivariate dynamic spatial data. Environmetrics, 16(5), 465–479. https://doi.org/10.1002/env.715
Gelfand, A. E., S. Banerjee, and D. Gamerman. “Spatial process modelling for univariate and multivariate dynamic spatial data.” Environmetrics 16, no. 5 (August 1, 2005): 465–79. https://doi.org/10.1002/env.715.
Gelfand AE, Banerjee S, Gamerman D. Spatial process modelling for univariate and multivariate dynamic spatial data. Environmetrics. 2005 Aug 1;16(5):465–79.
Gelfand, A. E., et al. “Spatial process modelling for univariate and multivariate dynamic spatial data.” Environmetrics, vol. 16, no. 5, Aug. 2005, pp. 465–79. Scopus, doi:10.1002/env.715.
Gelfand AE, Banerjee S, Gamerman D. Spatial process modelling for univariate and multivariate dynamic spatial data. Environmetrics. 2005 Aug 1;16(5):465–479.
Journal cover image

Published In

Environmetrics

DOI

ISSN

1180-4009

Publication Date

August 1, 2005

Volume

16

Issue

5

Start / End Page

465 / 479

Related Subject Headings

  • Statistics & Probability
  • 49 Mathematical sciences
  • 41 Environmental sciences
  • 05 Environmental Sciences
  • 01 Mathematical Sciences